Machine Learning Tutorial Python - 11 Random Forest

[@codebasics]

Check out our premium machine learning course with 2 Industry projects: codebasics.io/courses/machine-learning-for-data-science-beginners-to-advanced

[@vipulsemwal478]

Hi, I have a question regarding fitting the model. When we do model. fit in every training, there will be a random set of samples for training. For example, in the iris dataset, I fit my model and then fine-tune with n_estimators =10,20,100, etc. sometimes it is getting 1.0 score on 20, but if I run it again, it gets 0.98, so how can I fix the x_train and y_train so it will not change every time. ? And I am really thankful for your lectures I am learning day by day. Thank you.

[@sanchit0542]

Keeping the tutorial part aside (which is great), I really love your sense of humor and it's an amazing way to make the video more engaging. Kudos!! Also, thank you so much for imparting such great knowledge for free.

[@codebasics]

Thanks for your kind words and appreciation shankey 😊

[@zerostudy7508]

Lets promote this channel. I am just a humble python hobbies who took local course yet still I don't understand most of the lecturer says. Because this channel i've finally found fun with python. In just 2 weeks(more) I already this Level? Man....! Can't Wait for Neural Network but only from this channel

[@srujanjayraj9490]

The way you teach or explain the concepts completely different thanks a lot!!!!!! Please make more videos

[@kausikkar2587]

Sir, I am damn impressed by you!!!! You are the best ML instructor here on YT!!!!

[@roodrakanwar3300]

I achieved an accuracy of .9736. Earlier, I got an accuracy of .9 when the test size was 0.2 and changing the number of trees wasn't changing the accuracy much. So, I tweaked the test size to .25 and tried different number of tree size. The best I got was .9736 with n_estimators = 60 and criterion = entropy gives a better result. Thank you so much sir for the series. This is the best KZbin Series on Machine Learning out there!!

[@lokeshplssl8795]

xlabel is the truth and ylabel is the prediction but in the video it is reverse.... Am I right? because we take "confusion_matrix(y_test,y_predicted)"

[@panagiotisgoulas8539]

@@lokeshplssl8795 I think I know why you are probably confused. This not a plot chart. You should not assume that because you passed y_test as a first argument you would see it horizontally similarly you do with xlabel. Unfortunately the confusion matrix is printed out unlabeled. True/Actual/test values are vertically alligned and predicted ones are horizontally. A couple of videos before he used another library to demonstrate the matrix labeled. If you have any questions regarding confusion matrix this is by far the best video kzbin.info/www/bejne/boDSmGqKja2pfLs . Also a similar use case has to do with Bayesian statistics. Another great example kzbin.info/www/bejne/Y2LHioxqaKmFg6M You don't have to get into it since the software does it for you, but it would help understand what is going on

[@panagiotisgoulas8539]

It is a good practice to make a for loop for the n_estimators check the score for one of these: scores=[ ] n_estimators=range(1,51) #example for i in n_estimators : model=RandomForestClassifier(n_estimators=i) model.fit(X_train,y_train) scores.append(model.score(X_test,y_test)) print('score:{}, n_estimator:{}'.format(scores[i-1],i)) plt.plot(n_estimators,scores) plt.xlabel('n_estimators') plt.ylabel(('testing accuracy') And then you can sort of see what's going on. This practice is very useful for knearest neighbors technique for calculating k.

[@cololalo]

Thank you! I was looking for something like thi. I think in the fourth line the i is missing, as in model=RandomForestClassifier(n_estimators = i)

[@panagiotisgoulas8539]

@@cololalo yep forgot it thanks.

[@fathoniam8997]

Thank you, I am trying to find something like this since the previous video!

[@adityahpatel]

I cannot quite express how amazing teaching you are doing. I am doing masters one of the finest universities in America and this is better than the supervised learning class I am taking there. Kudos! Please keep it up. appreciate you are making this available for free although I would be willing to see your lectures even for a fee.

[@codebasics]

Thanks for leaving the feedback aditya

[@AbhishekSingh-og7kf]

I can watch this type of videos whole day without take any break. Thank you!!!

[@iaconst4.0]

eres un excelente profesor!, gracias por compartir tus conocimientos! saludos desde Peru!

[@rameshthamizhselvan2458]

frankly telling your videos are more neat and clear than anyother videos in the youtube

[@codebasics]

Thanks Ramesh for your valuable feedback :)

[@tanishqrastogi1011]

ok so i read one comment and put test_size = 0.25 and n_estimator = 60. I rerun my test sample cell as well as model.fit and model.predict cell and got the accuracy of 100%. I am having a god complex right now thank you for this amazing series

[@Pacificatorrr]

You sir, are a gem! Thank you for this series! I managed to get an accuracy of 98%!

[@motox296]

Great Video! I'm working on my first project using machine learning and am learning so much from your videos!

[@codebasics]

Hey Alex, good luck on your project buddy. I am glad these tutorials are helpful to you :)

[@Tuoc_Nguyen]

For Iris Datasets I got score =1 for n_estimators = 40,50,60 Thank sir very much

[@spicytuna08]

again, just spectacular graphics and easy to understand explanation. thank you so much.

[@pablu_7]

Thank you Sir for this awesome Explanation about RandomForestClassifier . I got score of 1.0 for every increased value in n_estimators

[@codebasics]

Nice work!

[@chrismagee5845]

FYI if you are using version 0.22 or later the default value of n_estimators changed from 10 to 100 in 0.22

[@vijaykumarlokhande1607]

this is crash course; if you are in hurry; this is the best series out there on youtube

[@codebasics]

github.com/codebasics/py/blob/master/ML/11_random_forest/Exercise/random_forest_exercise.ipynb Complete machine learning tutorial playlist: kzbin.info/www/bejne/nZ7Zp5Sll9Jqm7M

[@lokeshplssl8795]

xlabel is the truth and ylabel is the prediction but in the video it is reverse.... Am I right? because we take "confusion_matrix(y_test,y_predicted)"

[@jiyabyju]

@@lokeshplssl8795 I do have same question

[@lokeshplssl8795]

@@jiyabyju I figured it out

[@jiyabyju]

@@lokeshplssl8795 hope there is no mistake in code..

[@lokeshplssl8795]

@@jiyabyju no mistake, He took y_predicted as a model of prediction with X_test.

[@maruthiprasad8184]

I got 93.33 accuracy at n_estimators=30 after that accuracy not increasing w.r.t increase in n_estimators. Thankyou very much for simply great explanation

[@sumitkumarsain5542]

Just love ur videos. I was struggling with python. With ur videos was able to get everything in a weeks time. Also completed pandas and bumpy series. I would highly encourage u to start a machine learning course with some real life projects

[@anji1164]

Another Great Video. Thanks for that. I got 1.0 as score with n_estimators=1000. Keep doing these kind of great videos. Thank you.

[@codebasics]

Anji, it's great you are getting such an excellent score. Good job 👍👏

[@praveenkamble89]

I got 100% accuracy with default estimator and random_state=10. Thanks a lot Sir

[@codebasics]

Good job Praveen, that’s a pretty good score. Thanks for working on the exercise

[@abhinavsharma6633]

I got an accuracy of 0.982579 by giving, n_estimators = 100, well 100 is the default value now, and sir, big fan of your teaching 🙂

[@codebasics]

Good job Abhinav, that’s a pretty good score. Thanks for working on the exercise

[@abhinavsharma6633]

@@codebasics sir just wished to get in contact with you, to get a proper guidance

[@bhaskarg8438]

your teaching is superb, and your knowledge sharing to Data Science community is Nobe|. I tried the exercise by giving the criterion = "entropy" got score as 1

[@javadkhalilarjmandi3906]

I've done all the Exercise till here. But I was planning not to do it for this video until I saw your last picture! I don't want you to be angry! so I am going to do it right now!

[@codebasics]

Ha ha nice. Javad. Wish you all the best 🤓👍

[@devendragohare5221]

I Got 100% accuracy!.... by changing criterion = "entropy"

[@lokeshplssl8795]

xlabel is the truth and ylabel is the prediction but in the video it is reverse.... Am I right? because we take "confusion_matrix(y_test,y_predicted)"

[@carti8778]

@@lokeshplssl8795 it doesn’t change much, i mean u are just transposing the confusion matrix. The info still remain the same

[@Moukraan]

Thank you very much! This tutorial is really amazing!

[@sagnikmukherjee8954]

n_estimators = 10, criterion = 'entropy' led to a 100% accurate model !! Thanks!

[@codebasics]

Great job Sagnik :) Thanks for working on exercise

[@sagnikmukherjee8954]

@@codebasics My pleasure ! Amazing tutorials !! Been a great learning experience so far ! Cheers :)

[@jyothishp143]

This is the only channel i subscribed.

[@codebasics]

J Es, thanks. I am happy to have you as a subscriber 👍😊

[@MazlumDincer]

n_estimators = 1 (also 290 or bigger) is even made accuracy %100 but, as all we know , this type of datasets are prepared for learning phases, so making %100 accuracy is so easy as well.

[@lakshyasharma24]

Sir I got score=1.0 for estimator=10 And random_state=10 Very nice explanation👌👌👌

[@codebasics]

Great score. Good job 👌👏

[@usmanafridi9668]

Best explanation of Random Forest!!!!!!

[@codebasics]

I am happy this was helpful to you.

[@allahbakshsheikdawood466]

Nice to watch your videos.. you make us understand things end to end !!

[@codebasics]

👍😊

[@harshalbhoir8986]

This is so awesome explanation!! Thank you so much!!!

[@RustemShaimagambetov]

Man, its great! Your videos is best i have seen ever about machine learning. Its very helpfull material. I am waiting when you make tutorial about gradient boosting and neural networks. I think you can make easily to report it. Thanks!

[@igorsmet1123]

Thank you so much for very dynamic and clear content with the ideal depth on the topic details

[@codebasics]

Glad it was helpful!

[@rajmohammed8134]

Thank you for such wonderful videos, I got accuracy score a 1 in the exercise question

[@VivekKumar-li6xr]

Hello Sir, I have started learning pandas and ML from your channel, and i am amazed the way you are teaching. For Iris Datasets I got score =1 for n_estimators = 30

[@codebasics]

Great Vivek. I am glad you are working on exercise. Thanks 😊

[@geethanjaliravichandhran8109]

Hi sir,i did your exercise of iris data and got an accuracy of 1.0 with n_estimators=80

[@talharauf3111]

Sir I have Done the Exercise with 100% Accuracy

[@veeek8]

You made that so simple thank you so much

[@harishdange9048]

from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(n_estimators=30) rf.fit(X_train,Y_train) Output: RandomForestClassifier(n_estimators=30) rf.score(X_test,Y_test) output: 1.0 from sklearn.metrics import confusion_matrix cm = confusion_matrix(Y_test,Y_pred) cm output: array([[11, 0, 0], [ 0, 8, 0], [ 0, 0, 11]], dtype=int64)

[@ashish31416]

You are amazing brother. I really loved this. You made it so simple. Thank you so much.

[@James-pe3wl]

Maybe I am a bit late jumping on the train, even though, I still want to say thank you for everything you have been doing. Your videos are much better to understand the field rather than the courses of top class Universities such as MIT. I have to say that you outperform all your competitors in a very simple way. As far as I know you had some problems with your health and I hope everything is good now. Wish you good luck and stay healthy at least for your KZbin community. ^_^

[@codebasics]

Hey Yea James, thanks for checking on my health. You are right, I was suffering from chronic ulcerative colitis and last year 2019 had been pretty rought. But guess what I cured it using raw vegan diet, ayurveda and homeopathy. I am 100% all right and symptoms free since past 10 months almost and back in full force doing youtube tutorials :)

[@prvs2004]

@@codebasics Good to hear, Things are working out in a positive way! Be safe and I pray everything works well in the long run. Jai SriRam

[@ashishsinha8893]

It's nice to see you bhaiya again

[@ajaykumaars2154]

Hi Sir, Can we use any other model (eg: svm) with the random forest approach, that is, by creating an ensemble out of 10 svm models and getting a majority vote? Thank you for the wonderful video.

[@rajatbhalla1455]

Sir u r great thnx for these kinds of videos please make more videos 😊😊😊😊

[@rajatbhalla1455]

Sir make more videos

[@vishank7]

This is sooo awesome! Amazing work sir💎

[@tk1215]

Amazing, I like how you explain simply

[@muhammedrajab2301]

I am not afraid of you, but I respect you! So I am gonna do the exercise right now!

[@shivamtyagi5614]

default 100 n_estimators or 20 n_estimator , each case it gives 1.0 accuracy. well after getting on this channel , i can feel the warmth on the tip of my fingers.

[@adarshkesarwani6775]

Thanks a lot sir for the videos, I wanna know when to use random forest or just tree?

[@sohamnavadiya992]

Amazing man, keep it up and share more tutorial like this.

[@alokpratap2094]

Again a nice video from you. Sir I have one general question. What is random_state and why we sometime take 0 and sometimes we assign value to it. What's the significance of this.

[@vijaydas2962]

Thanks for another post.. It's really helpful.... Just a question- Considering the fact that Random forest takes the majority decision from multiple decision trees, does it imply that Random forest is better than using Decision tree algorithm? How do we decide when to use Decision tree versus Random forest?

[@kpl_sh]

Thank you sir...I got 100% accuracy with n_estimator 90

[@codebasics]

Good job Kapil, that’s a pretty good score. Thanks for working on the exercise

[@ousmanelom6274]

thank you for this tutorial how to visualize randomforest and decision tree

[@AlvinHampton-rz2iz]

What makes you put truth on the y_label and predicted on the x_label?

[@mycreations3452]

Please upload frequently..we will wait for you

[@VIVEK-ld3ey]

Sir how are you deciding the xlabel and ylabel in the heatmap

[@jaydhumal2610]

I got the perfect score of 1 when I set n_estimators to 40 although the selection of train,test data would also have been contributed in the accuracy of model.

[@ericwr4965]

Thank you so much. I need some help on this classifier for my data set. This helped a lot.

[@codebasics]

Glad it helped!

[@freecodecamp]

This is a great series! Would you be interested in allowing us to repost it on our channel? We'll link to your channel in the description and comment section. Send me an email to discuss further: beau [at] [channelname]

[@ShubhamSharma-to5po]

mega.nz/file/LaozDBrI#iDkMIu6v-aL9fMsl-X1DETkOqnMqwptkn54Z51KINyw (like data in this file )//help if anyone understand. mega.nz/file/LaozDBrI#iDkMIu6v-aL9fMsl-X1DETkOqnMqwptkn54Z51KINyw (like data in this file )//help if anyone understand.

[@ShubhamSharma-to5po]

sir, can you tell me how to plot random forest classification with multiple independent variables.so confused in that

[@codebasics]

yes sure. go ahead. You can post it.

[@aishwaryakilledar1742]

Very nice sir.... Expecting more videos 😀

[@codebasics]

Glad it was helpful!

[@granothon8054]

Excellent. Thank you.

[@dickson9877]

I see many people is saying that in Irises they had 1.0 with 50+ esitmators. I am just starting with ML but for me 4 functions in Irises means that we don't need much estimators, there is actually only 6 unique combinations of functions. 10 if we used also solo columns as estimators which I presume is not happening. Am I correct that anything beyond 6 estimators shouldn't improve the model?

[@hanfeng32]

very Great video!!!!! thanks

[@codebasics]

glad you liked it Han

[@shukur533]

train_test_split test size is 20% and the random state is 32 1. n_estimators default test score is 0.96 2. The best test score is 1.0 and n_estimators is 3

[@bikram40]

Nice explanation as always. Great work.

[@meenakshimalik7102]

Hi Sir, we are blessed that we got your videos on youtube. Your videos are unmatchable. I am interested in your upcoming python course. When can I expect starting of the course?

[@codebasics]

Python course is launching in June, 2022. Not sure about exact date though

[@pranaymitra7565]

Great content!! I have a question though, shouldn't the xlabel be 'Truth' and ylabel be 'Predicted' ?

[@iam_Oteknonso]

i though the same thing as well

[@iradukundapacifique987]

100% accuracy on the given exercise. I used n_estimators = 1

[@codebasics]

That’s the way to go Iradukunda, good job working on that exercise

[@mohamedabouobayd1992]

Love your videos. They're helping me a lot. thanks

[@codebasics]

Hey Mohamed, Thanks for nice comment. Stay in touch for more videos.

[@RubiPandey-l6j]

You r God for me for helping me phd

[@codebasics]

🙌Woohoo! So glad it hit the mark for you! 😃

[@jeminceman4211]

This video was amazing. Thanks!

[@jyothishp143]

Nice videos, Your videos are the best..Keep doing

[@codebasics]

Jyothish, I am happy this was helpful to you.

[@usamarehmanyousaf2010]

Hi, just want to ask this question that, in a data set split why should we drop the target column. Like that is the actual or final result that either the row is true or false. Then while spliting why should we have to drop that?

[@vaishalibhat3741]

test_size=0.2 model=RandomForestClassifier(n_estimators=10,criterion='gini') model.score = 1 2) RandomForestClassifier(criterion='entropy', n_estimators=10) model.score = 1 3) test_size=0.35 RandomForestClassifier(n_estimators=10,criterion='gini') model.score=0.9811 4) test_size=0.35 RandomForestClassifier(n_estimators=10,criterion='entropy') model.score=0.9811

[@dineshjangra7413]

Way of teaching is very good.....sir plz make a vedio on how to give our image to it....how to convert our image like mnist dataset as there is benefit till the time we will use our images

[@codebasics]

Sure I am going to add image classification tutorial.

[@dineshjangra7413]

@@codebasics thanks sir

[@larrybuluma2458]

Thank you so much for this tutorial my accuracy score is 0.9666667 with n_estimators at 40

[@codebasics]

That's a great score larry. Good job 👍👏

[@aashikasharma1616]

I didn't understand the "number of random forests".

[@supra20000000]

The R2 we got is for test set (R2test), what about the model's R2 which is generally termed as R2training

[@unamattina6023]

i don't see RandomForestClassifier parameters just like you do, it is just RandomForestClassifier() for me at 8.25. What I am missing?

[@КоробкаРобота]

I got 0.9333 with 90 trees. Thanks!

[@codebasics]

Good job Коробка, that’s a pretty good score. Thanks for working on the exercise

[@boooringlearning]

excellent lesson!

[@codebasics]

Glad it was helpful!

[@2015murat]

great videos! thank you so much

[@jrajpal5]

Hi sir, i have a simple query regarding jupyter notebook. I can't see the parameters of randomforestclassifier() after applying model.fit() Is there any way to see those parameters

[@sonikusum3]

I am new to machine learning. Why am I not getting the same numbers as you did for the confusion matrix or scores? I used exactly the same coding as in the video.

[@saltsea9499]

Does sklearn offer a validation training method?

[@late_nights]

The default value of n_estimators changed from 10 to 100 in 0.22 version of skllearn. i got accuracy of 95.56 with n_estimators = 10 and for 100 the same.

[@izharkhankhattak]

Nice work.

[@ahmedakmal1545]

I got 100% accuracy after tuning the parameters and train test split for the iris dataset test_size=0.2, n_estimators=20, random_state=2

[@anujvyas9493]

Solved the exercise problem. With model = RandomForestClassifier(n_estimators=10) got an accuracy of 0.96667 and with model = RandomForestClassifier(n_estimators=20) got 1.0

[@codebasics]

Anuj, good job 👍👏👌

[@anujvyas9493]

@@codebasics Thanks sir! Its all because of you 😊

[@snehasneha9290]

sir suppose to consider the 4 decision trees in that 2 trees give the same output and another 2 trees give the same output then which one considered both having the majority at that time plz clarify this doubt

[@aliasjad9560]

this video is very helpful .

[@codebasics]

Glad it was helpful!

[@ogochukwustanleyikegbo2420]

I did the exercise and I got a score of 0.9 with 20 estimators, 0.93 with 50 estimators and 0.9 with 100 estimators

[@arijitRC473]

Result of exercise: Score is always 96.66 percent If i will change n_estimators or will increase it, the score is not changing

[@IntegralKing]

are you re-running the fit? because it fit doesn't automatically rerun after changing the parameters

[@jessehahka]

Is it possible to predict a set of numbers that will output from a random number generator, finding the algorithm, in order to duplicate the same pattern of results?